| Title |
Evolving-Pattern Analysis of Transient and Long-Term Biomarkers for Cancers: Hepatocellular Carcinoma as a Case
|
|---|---|
| Published in |
Interdisciplinary Sciences: Computational Life Sciences, August 2015
|
| DOI | 10.1007/s12539-015-0276-7 |
| Pubmed ID | |
| Authors |
Yingying Wang, Yunpeng Cai, Yingbo Miao |
| Abstract |
Cancer is a complex disease arises from combinations of changes that occur over a period of time. With the development of bioinformatics, more and more biomarkers representing changes in cancers had been identified using gene expression profiles. However, biomarkers alone are quite limited in explaining the molecular processes occurred in the due process. In this paper, we develop an evolving-pattern analysis pipeline for in-depth studies of gene expression changes during different disease stages, choosing hepatocellular carcinoma (HCC) as a case. Enrichment analyses were performed on three levels: functional terms, validated genes, and regulation factors for all the biomarkers to find out their biological characters. Our results show that biomarkers with distinct evolving patterns exhibit quite different characteristics on functional and regulation levels. For the case of HCC, transient biomarkers are mostly annotated to metabolic processes, while long-term biomarkers are mostly annotated to regulation processes, with a larger number of enriched regulation factors. Furthermore, our pipeline reveals the important roles of microRNAs in various evolving patterns, which are known to be closely related to HCC. These results confirm that evolving-pattern analysis may provide a new sight for in-depth studies of biomarkers and diseases. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 6 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 1 | 17% |
| Researcher | 1 | 17% |
| Student > Postgraduate | 1 | 17% |
| Student > Doctoral Student | 1 | 17% |
| Unknown | 2 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 2 | 33% |
| Agricultural and Biological Sciences | 2 | 33% |
| Biochemistry, Genetics and Molecular Biology | 1 | 17% |
| Unknown | 1 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 August 2015.
All research outputs
#14,821,227
of 22,821,814 outputs
Outputs from Interdisciplinary Sciences: Computational Life Sciences
#97
of 294 outputs
Outputs of similar age
#145,819
of 264,494 outputs
Outputs of similar age from Interdisciplinary Sciences: Computational Life Sciences
#7
of 26 outputs
Altmetric has tracked 22,821,814 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 294 research outputs from this source. They receive a mean Attention Score of 2.9. This one has gotten more attention than average, scoring higher than 62% of its peers.
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We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.